Visual odometry refers to the problem of determining a motion of a moving object, e.g., a vehicle or a robot, from one position to another using features from images acquired by, e.g., one or more camera mounted on the object. Numerous methods are known for estimating the motion using geometric primitives, such as points. For example, one can determine the motion using correspondences between 2D points in one coordinate system to another coordinate system using 5 or more point correspondences, see Nister, “An efficient solution to the five-point relative pose problem,” PAMI, 2004.
There are also 2D to 3D pose estimation methods used to estimate the position of a moving object based on partial 3D point cloud reconstructed by the previous stereo images, Kitt et al., “Visual odometry based on stereo image sequences with ransac-based outlier rejection scheme,” IV, 2010.
One method uses 3D to 3D point correspondences in an iterative closest point (ICP) method for determining motion between stereo cameras, Milella et al., “Stereo-based ego-motion estimation using pixel tracking and iterative closest point, ICCVS, 2006.
Voting strategies have been used for computing visual odometry, U.S. 20120308114, “Voting strategy for visual ego-motion from stereo,” and machine learning procedures have been used to estimate the camera pose WO 2014130404, “Method and device for calculating a camera or object pose.”